Refinements of Aczél-Type Inequality and Their Applications

We present some new sharpened versions of Aczél-type inequality. Moreover, as applications, some refinements of integral type of Aczél-type inequality are given

Refinements of Hardy-Type Inequalities

Using Hu Ke's inequality, which is a sharped Hölder's inequality, we present some new refinements of Hardy-type inequalities proposed by Imoru

Perturbed autophagy intervenes systemic lupus erythematosus by active ingredients of traditional Chinese medicine

Systemic lupus erythematosus (SLE) is a common multisystem, multiorgan heterozygous autoimmune disease. The main pathological features of the disease are autoantibody production and immune complex deposition. Autophagy is an important mechanism to maintain cell homeostasis. Autophagy functional abnormalities lead to the accumulation of apoptosis and induce the autoantibodies that result in immune disorders. Therefore, improving autophagy may alleviate the development of SLE. For SLE, glucocorticoids or immunosuppressive agents are commonly used in clinical treatment, but long-term use of these drugs causes serious side effects in humans. Immunosuppressive agents are expensive. Traditional Chinese medicines (TCMs) are widely used for immune diseases due to their low toxicity and few side effects. Many recent studies found that TCM and its active ingredients affected the pathological development of SLE by regulating autophagy. This article explains how autophagy interferes with immune system homeostasis and participates in the occurrence and development of SLE. It also summarizes several studies on TCM-regulated autophagy intervention in SLE to generate new ideas for basic research, the development of novel medications, and the clinical treatment of SLE

Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis

Phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating photosynthesis and other ecosystem processes, as well as competitive interactions and feedbacks to the climate system. We conducted an analysis to evaluate the representation of phenology, and the associated seasonality of ecosystem-scale CO2 exchange, in 14 models participating in the North American Carbon Program Site Synthesis. Model predictions were evaluated using long-term measurements (emphasizing the period 2000-2006) from 10 forested sites within the AmeriFlux and Fluxnet-Canada networks. In deciduous forests, almost all models consistently predicted that the growing season started earlier, and ended later, than was actually observed; biases of 2 weeks or more were typical. For these sites, most models were also unable to explain more than a small fraction of the observed interannual variability in phenological transition dates. Finally, for deciduous forests, misrepresentation of the seasonal cycle resulted in over-prediction of gross ecosystem photosynthesis by +160 ± 145 g C m-2 y-1 during the spring transition period, and +75 ± 130 g C m-2 y-1 during the autumn transition period (13% and 8% annual productivity, respectively) compensating for the tendency of most models to under-predict the magnitude of peak summertime photosynthetic rates. Models did a better job of predicting the seasonality of CO2 exchange for evergreen forests. These results highlight the need for improved understanding of the environmental controls on vegetation phenology, and incorporation of this knowledge into better phenological models. Existing models are unlikely to predict future responses of phenology to climate change accurately, and therefore will misrepresent the seasonality and interannual variability of key biosphere-atmosphere feedbacks and interactions in coupled global climate models.Engineering and Applied SciencesOrganismic and Evolutionary Biolog

Evaluation of continental carbon cycle simulations with North American flux tower observations

Terrestrial biosphere models can help identify physical processes that control carbon dynamics, including land–atmosphere CO₂ fluxes, and have great potential to predict the terrestrial ecosystem response to changing climate. The skill of models that provide continental-scale carbon flux estimates, however, remains largely untested. This paper evaluates the performance of continental-scale flux estimates from 17 models against observations from 36 North American flux towers. Fluxes extracted from regional model simulations were compared with co-located flux tower observations at monthly and annual time increments. Site-level model simulations were used to help interpret sources of the mismatch between the regional simulations and site-based observations. On average, the regional model runs overestimated the annual gross primary productivity (5%) and total respiration (15%), and they significantly underestimated the annual net carbon uptake (64%) during the time period 2000–2005. Comparison with site-level simulations implicated choices specific to regional model simulations as contributors to the gross flux biases, but not the net carbon uptake bias. The models performed the best at simulating carbon exchange at deciduous broadleaf sites, likely because a number of models used prescribed phenology to simulate seasonal fluxes. The models did not perform as well for crop, grass, and evergreen sites. The regional models matched the observations most closely in terms of seasonal correlation and seasonal magnitude of variation, but they have very little skill at interannual correlation and minimal skill at interannual magnitude of variability. The comparison of site vs. regional-level model runs demonstrated that (1) the interannual correlation is higher for site-level model runs, but the skill remains low; and (2) the underestimation of year-to-year variability for all fluxes is an inherent weakness of the models. The best-performing regional models that did not use flux tower calibration were CLM-CN, CASA-GFEDv2, and SIB3.1. Two flux tower calibrated, empirical models, EC-MOD and MOD17+, performed as well as the best process-based models. This suggests that (1) empirical, calibrated models can perform as well as complex, process-based models and (2) combining process-based model structure with relevant constraining data could significantly improve model performance.Keywords: model–data comparison, flux towers, terrestrial biosphere models, carbon fluxesKeywords: model–data comparison, flux towers, terrestrial biosphere models, carbon fluxe

Moment Estimation Inequalities Based on <inline-formula> <graphic file="1029-242X-2010-290124-i1.gif"/></inline-formula> Random Variable on Sugeno Measure Space

<p/> <p>The definitions and properties of moment of <inline-formula> <graphic file="1029-242X-2010-290124-i2.gif"/></inline-formula> random variable are provided on Sugeno measure space. Then some important moment estimation inequalities based on <inline-formula> <graphic file="1029-242X-2010-290124-i3.gif"/></inline-formula> random variable are presented and proven.</p